Dispersion of soybean stock‐based nanofiber in a plastic matrix
Bibliographic record
Abstract
Abstract The focus of this work is the study of the dispersion mechanism of soybean stock‐based nanofibers in a plastic matrix. The cellulose nanofibers were extracted from soybean stock by chemo‐mechanical treatments. These are bundles of cellulose nanofibers with a diameter ranging between 50 and 100 nm and lengths of thousands of nanometers. These nanofibers were characterized by atomic force microscopy and transmission electron microscopy. X‐ray diffraction studies showed that the soybean stock nanofibers had a relative percentage crystallinity of about 48%. Selective chemical treatments increased the cellulose content of soybean stock nanofibers from 41 to 61%. The matrix polymers used in this project were poly(vinyl alcohol) (PVA) and polyethylene (PE). The mechanical properties of nanofiber‐reinforced PVA film demonstrated a 4‐ to 5‐fold increase in tensile strength, as compared to the untreated fiber‐ blend ‐PVA film. One of the problems encountered in the use of nanoreinforcements lies in the difficulty in ensuring good dispersion of the filler in the composite material. Improved dispersion level of nanofibers within a thermoplastic was achieved by adding ethylene‐acrylic oligomer emulsion as a dispersant. In the solid phase of nanofiber‐ blend ‐PE composites, the compression‐molded samples showed that improved mechanical properties were achieved with coated nanofibers. Copyright © 2006 Society of Chemical Industry
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".